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    IBM Watson: A new support tool for healthcare
    Abstract
    Watson, a computer system named after founder of IBM, won the championship in Jeopardy!. The success showed its ability of processing natural language, which hinted the application of Watson in healthcare. It can help physicians decide diagnosis or treatment, meet science information requirements and communicate with other physicians worldwide. It can be utilized by individuals and health organizations as well. Nevertheless, several significant challenges are required to overcome in order that Watson can be applied to healthcare support tool in practice.
     
    Watson, a computer system developed by IBM scientists, is able to answer questions posed in human language with high speed and accuracy. In 2011, it won the championship in Jeopardy!, which is an American quiz show featuring multifarious questions in natural language. In its first appearance on Jeopardy!, Watson dealt with the similar amount of 1 million books to provide the most accurate result to a certain question (Harvie & McGoff, 2012). The success hints the extensive application of Watson in dramatically distinct and excessively specialized fields like business and healthcare. Healthcare is one of the most import aspects that Watson can be applied to.
    With the ability of comprehending human language, processing information and finding exact answers, Watson owns the potential to understand information from patients and give out answers such as probable diagnosis or treatments. One of the problems that healthcare faces with is evitable diagnostic mistakes in healthcare process (Graber, Franklin & Gordon, 2005; Croskerry, 2009). The rate of antemortem diagnostic discrepancy detected through autopsy is about 20% to 40%, a third of which would have been avoided by accurate diagnosis (Gawande, 2002). Diagnostic errors are classified into no-fault errors, system errors and cognitive errors (Graber, Gordon & Franklin, 2002). To minimize errors caused by mistakes of physicians’ cognition, the application of computer-based support systems has been regarded as an ideal solution (Graber et al., 2002). Several computer-based support tools have been developed for decades including DXplain (Barnett et al., 1987) and Isabel (Ramnarayan et al., 2007). However, it seems that the misdiagnosis rate has not decreased significantly with improved technology (Croskerry, 2009). Limited knowledge base, prolonged consultation, and high requirement for diagnostic information limit the wide use of these systems (Ramnarayan et al., 2007).

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